<<<<<<< HEAD assumptionChecking
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.5     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.0.2     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(dplyr)
library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
library(leaps)
library(ggfortify)
data = read.table("winequality-red.csv", sep=";", header=T)

1 ALCOHOL VS QUALITY

1.1 Linearity

lm1 = lm(quality ~ alcohol, data = data)
par(cex = 0.5)
plot(quality ~ alcohol, data = data)
abline(lm1, lwd = 3, col = 'red')

1.2 Residuals and Normalitys

autoplot(lm1, which = 1:2)

2 SULFATES VS QUALITY

# SULPHATES LINEARITY
lm1 = lm(quality ~ sulphates, data = data)
par(cex = 0.5)
plot(quality ~ sulphates, data = data)
abline(lm1, lwd = 3, col = 'red')

2.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

3 VOLATILE ACIDITY VS QUALITY

# VOLATILE ACIDITY LINEARITY
lm1 = lm(quality ~ volatile.acidity, data = data)
par(cex = 0.5)
plot(quality ~ volatile.acidity, data = data)
abline(lm1, lwd = 3, col = 'red')

3.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

4 TOTAL SULFUR DIOXIDE VS QUALITY

# TOTAL SULFUR DIOXIDE LINEARITY
lm1 = lm(quality ~ total.sulfur.dioxide, data = data)
par(cex = 0.5)
plot(quality ~ total.sulfur.dioxide, data = data)
abline(lm1, lwd = 3, col = 'red')

4.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

5 FREE SULFUR DIOXIDE VS QUALITY

# FREE SULFUR DIOXIDE LINEARITY
lm1 = lm(quality ~ free.sulfur.dioxide, data = data)
par(cex = 0.5)
plot(quality ~ free.sulfur.dioxide, data = data)
abline(lm1, lwd = 3, col = 'red')

## Residuals and Normalitys

autoplot(lm1, which = 1:2)

6 CHLORIDES VS QUALITY

# CHLORIDES LINEARITY
lm1 = lm(quality ~ chlorides, data = data)
par(cex = 0.5)
plot(quality ~ chlorides, data = data)
abline(lm1, lwd = 3, col = 'red')

6.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

7 PH VS QUALITY

# pH LINEARITY
lm1 = lm(quality ~ pH, data = data)
par(cex = 0.5)
plot(quality ~ pH, data = data)
abline(lm1, lwd = 3, col = 'red')

7.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

8 CITRIC ACID VS QUALITY

# citric.acid LINEARITY
lm1 = lm(quality ~ citric.acid, data = data)
par(cex = 0.5)
plot(quality ~ citric.acid, data = data)
abline(lm1, lwd = 3, col = 'red')

8.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

9 RESIDUAL SUGAR VS QUALITY

# residual.sugar LINEARITY
lm1 = lm(quality ~ residual.sugar, data = data)
par(cex = 0.5)
plot(quality ~ residual.sugar, data = data)
abline(lm1, lwd = 3, col = 'red')

9.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

10 FIXED.ACIDITY VS QUALITY

# fixed.acidity LINEARITY
lm1 = lm(quality ~ fixed.acidity, data = data)
par(cex = 0.5)
plot(quality ~ fixed.acidity, data = data)
abline(lm1, lwd = 3, col = 'red')

## Residuals and Normalitys

autoplot(lm1, which = 1:2)

11 DENSITY VS QUALITY

# density LINEARITY
lm1 = lm(quality ~ density, data = data)
par(cex = 0.5)
plot(quality ~ density, data = data)
abline(lm1, lwd = 3, col = 'red')

11.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

======= assumptionChecking
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.1
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.3     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(dplyr)
library(GGally)
## Warning: package 'GGally' was built under R version 4.1.1
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
library(leaps)
## Warning: package 'leaps' was built under R version 4.1.1
library(ggfortify)
## Warning: package 'ggfortify' was built under R version 4.1.1
data = read.table("winequality-red.csv", sep=";", header=T)

1 ALCOHOL VS QUALITY

1.1 Linearity

lm1 = lm(quality ~ alcohol, data = data)
par(cex = 0.5)
plot(quality ~ alcohol, data = data)
abline(lm1, lwd = 3, col = 'red')

1.2 Residuals and Normalitys

autoplot(lm1, which = 1:2)

2 SULFATES VS QUALITY

# SULPHATES LINEARITY
lm1 = lm(quality ~ log(sulphates, 30), data = data)
par(cex = 0.5)
plot(quality ~ log(sulphates, 30), data = data)
abline(lm1, lwd = 3, col = 'red')

2.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

3 VOLATILE ACIDITY VS QUALITY

# VOLATILE ACIDITY LINEARITY
lm1 = lm(quality ~ log(sqrt((volatile.acidity))), data = data)
par(cex = 0.5)
plot(quality ~ log(sqrt((volatile.acidity))), data = data)
abline(lm1, lwd = 3, col = 'red')

3.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

4 TOTAL SULFUR DIOXIDE VS QUALITY

# TOTAL SULFUR DIOXIDE LINEARITY
lm1 = lm(quality ~ log(total.sulfur.dioxide), data = data)
par(cex = 0.5)
plot(quality ~ log(total.sulfur.dioxide), data = data)
abline(lm1, lwd = 3, col = 'red')

4.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

5 FREE SULFUR DIOXIDE VS QUALITY

# FREE SULFUR DIOXIDE LINEARITY
lm1 = lm(quality ~ free.sulfur.dioxide, data = data)
par(cex = 0.5)
plot(quality ~ free.sulfur.dioxide, data = data)
abline(lm1, lwd = 3, col = 'red')

## Residuals and Normalitys

autoplot(lm1, which = 1:2)

6 CHLORIDES VS QUALITY

# CHLORIDES LINEARITY
lm1 = lm(quality ~ chlorides, data = data)
par(cex = 0.5)
plot(quality ~ chlorides, data = data)
abline(lm1, lwd = 3, col = 'red')

6.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

7 PH VS QUALITY

# pH LINEARITY
lm1 = lm(quality ~ I(pH^2), data = data)
par(cex = 0.5)
plot(quality ~ I(pH^2), data = data)
abline(lm1, lwd = 3, col = 'red')

7.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

8 CITRIC ACID VS QUALITY

# citric.acid LINEARITY
lm1 = lm(quality ~ citric.acid, data = data)
par(cex = 0.5)
plot(quality ~ citric.acid, data = data)
abline(lm1, lwd = 3, col = 'red')

8.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

9 RESIDUAL SUGAR VS QUALITY

# residual.sugar LINEARITY
lm1 = lm(quality ~ residual.sugar, data = data)
par(cex = 0.5)
plot(quality ~ residual.sugar, data = data)
abline(lm1, lwd = 3, col = 'red')

9.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

10 FIXED.ACIDITY VS QUALITY

# fixed.acidity LINEARITY
lm1 = lm(quality ~ fixed.acidity, data = data)
par(cex = 0.5)
plot(quality ~ fixed.acidity, data = data)
abline(lm1, lwd = 3, col = 'red')

## Residuals and Normalitys

autoplot(lm1, which = 1:2)

11 DENSITY VS QUALITY

# density LINEARITY
lm1 = lm(quality ~ density, data = data)
par(cex = 0.5)
plot(quality ~ density, data = data)
abline(lm1, lwd = 3, col = 'red')

11.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

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